# Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh

arXiv: https://arxiv.org/abs/2212.09144 · HTML: https://mlsanity.com/paper/2212.09144/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 18 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification](https://mlsanity.com/paper/2109.02394/) (2021)
- [Two Decades of Bengali Handwritten Digit Recognition: A Survey](https://mlsanity.com/paper/2206.02234/) (2022)
- [Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis](https://mlsanity.com/paper/2007.16198/) (2020)
- [Densely-Populated Traffic Detection using YOLOv5 and Non-Maximum Suppression Ensembling](https://mlsanity.com/paper/2108.12118/) (2021)
- [Real-time Bangla License Plate Recognition System for Low Resource Video-based Applications](https://mlsanity.com/paper/2108.08339/) (2021)
- [Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study](https://mlsanity.com/paper/1601.01145/) (2016)

## Built on by (most cited first, 4 of 4)

- [An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification](https://mlsanity.com/paper/2304.06520/) (2023)
- [Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition](https://mlsanity.com/paper/2506.00447/) (2025)
- [Bangladeshi Native Vehicle Detection in Wild](https://mlsanity.com/paper/2405.12150/) (2024)
- [Finetuning YOLOv9 for Vehicle Detection: Deep Learning for Intelligent Transportation Systems in Dhaka, Bangladesh](https://mlsanity.com/paper/2410.08230/) (2024)

## Abstract (arXiv)

The task of locating and classifying different types of vehicles has become a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification and many more. In recent times, Deep Learning models have been dominating the field of vehicle detection. Yet, Bangladeshi vehicle detection has remained a relatively unexplored area. One of the main goals of vehicle detection is its real-time application, where `You Only Look Once' (YOLO) models have proven to be the most effective architecture. In this work, intending to find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh, we have conducted a performance analysis of different variants of the YOLO-based architectures such as YOLOV3, YOLOV5s, and YOLOV5x. The models were trained on a dataset containing 7390 images belonging to 21 types of vehicles comprising samples from the DhakaAI dataset, the Poribohon-BD dataset, and our self-collected images. After thorough quantitative and qualitative analysis, we found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.